funGp: Gaussian Process Models for Scalar and Functional Inputs
Construction and smart selection of Gaussian process models
with emphasis on treatment of functional inputs. This package
offers: (i) flexible modeling of functional-input regression
problems through the fairly general Gaussian process model; (ii)
built-in dimension reduction for functional inputs; (iii)
heuristic optimization of the structural parameters of the model
(e.g., active inputs, kernel function, type of distance).
Metamodeling background is provided in
Betancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>.
The algorithm for structural parameter optimization is described
in <https://hal.archives-ouvertes.fr/hal-02532713>.
| Version: |
0.2.2 |
| Imports: |
methods, foreach, knitr, scales, qdapRegex, microbenchmark, doFuture, future, progressr |
| Published: |
2021-07-22 |
| Author: |
Jose Betancourt [cre, aut],
François Bachoc [aut],
Thierry Klein [aut],
Deborah Idier [ctb],
Jeremy Rohmer [ctb] |
| Maintainer: |
Jose Betancourt <djbetancourt at uninorte.edu.co> |
| License: |
GPL-3 |
| URL: |
https://djbetancourt-gh.github.io/funGp/ |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
funGp results |
Documentation:
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